I-UNIT

Database Concepts:Database Systems - Data vs Information - Introducing the

database -File system -Problems with file system – Database systems. Data
models - Importance - Basic Building Blocks -Business rules - Evolution of Data
models - Degrees of Data Abstraction.

Introduction

Data:

 Data is a collection of raw facts and figures that do not have any meaning on
their own, but can be processed to produce useful information.

 The facts that can be recorded and which have implicit meaning known as
'data'.

Example:

Customer

1.cname.

2.cno.

3.ccity.

Database:

 It is a collection of interrelated data.

 These can be stored in the form of tables.

 A database can be of any size and varying complexity.

 A database may be generated and manipulated manually or it may be

computerized.

Example:

 Customer database consists the fields as cname, cno, and ccity

Database System:

 It is computerized system, whose overall purpose is to maintain the

information and to make that the information is available on demand.

Advantages:

 Redundancy can be reduced.

 Inconsistency can be avoided.
 Data can be shared.
 Standards can be enforced.
  Security restrictions can be applied.
 Integrity can be maintained.
 Data gathering can be possible.
 Requirements can be balanced.

DBMS

 A database-management system (DBMS) is a collection of interrelated data

and a set of programs to access those data.
 The collection of data, usually referred to as the database, contains
information relevant to an enterprise.

Data vs Information

Definition of Data:

Data is a raw, unorganized fact or value that has no meaning by itself.

Examples:

 25, "Alice", "Blue", 98.6

Definition of Information:

Information is processed or organized data that is meaningful and useful for decision-
making.

Examples:

 "Alice is 25 years old"

 "The temperature is 98.6°F"

Introduction to Database

What is a Database?

A Database is an organized collection of related data that can be easily accessed,

managed, and updated.

Examples of Databases:

o Library Database – Stores book records, members, and issue history

o Bank Database – Stores customer info, account details, transactions
o School Database – Stores student records, marks, attendance

Need for a Database:

o To store large amounts of data systematically

o To avoid data duplication (redundancy)
o To ensure data accuracy (consistency)
o To retrieve data quickly
 o To share data among multiple users

Components of a Database System:

o Data – Raw facts stored in the database

o DBMS – Software to manage the database
o Users – People who use the database system
o Hardware – Devices on which the database runs

Database Table Example:

StudentID Name Class Marks

101 Ravi 10 87

102 Meena 10 92

Advantages of Using a Database:

o Better data management

o Data sharing among users
o Security and access control
o Backup and recovery options
o Ensures data integrity

File system

A file system is a traditional method of storing and organizing data in files on a

disk.

Example:

o Student records stored in a .txt file

101, Ravi, 10, 85

102, Meena, 10, 90

Drawbacks in File System

There are so many drawbacks in using the file system. These are mentioned below −

 Data redundancy and inconsistency: Different file formats, duplication of

information in different files.

 Difficulty in accessing data: To carry out new task we need to write a new
program.

 Data Isolation − Different files and formats.

 Integrity problems.
  Atomicity of updates − Failures leave the database in an inconsistent state.
For example, the fund transfer from one account to another may be
incomplete.

 Concurrent access by multiple users.

 Security problems.

Database system offers so many solutions to all these problems

What is a Database System?

A Database System is a combination of database and DBMS (Database

Management System) that stores, manages, and processes data efficiently.

Components:

1. Database – Organized data

2. DBMS – Software to manage data
3. Users – Admins, developers, end users
4. Hardware & Software – For storing and accessing data

What is a Data Model?

A Data Model is a way to define, structure, and organize data. It shows how
data is connected and how it can be stored, retrieved, and updated.

Purpose of Data Models:

 To represent the logical structure of a database

 To define the relationships among data
 To help in designing the schema (database structure)

Types of Data Models in DBMS

Data Model Type Description Example

Data organized in a tree-like Employee →

Hierarchical Model
structure (parent-child) Department

Data in a graph structure (many- Student ↔

Network Model
to-many relationships) Course

Data stored in tables (relations) SQL Databases

Relational Model
with rows and columns like MySQL

Entity-Relationship Uses entities, attributes, and

ER diagrams
(ER) Model relationships
Data Model Type Description Example

Object-Oriented Data represented as objects (like Multimedia

Model in OOP) databases

Document/Data For unstructured/semi-

MongoDB
Models (NoSQL) structured data like JSON/XML

Most Popular: Relational Data Model

 Proposed by E. F. Codd
 Data stored in tables (relations)
 Uses primary keys and foreign keys to relate data
 Supports SQL (Structured Query Language)

ER Model (Entity-Relationship Model)

o Used in database design

o Key components:

Entity – Real-world object (e.g., Student)

Attribute – Properties (e.g., Name, Age)

Relationship – Association between entities (e.g., Enrolls)

Importance of Data Models in DBMS:

Importance Explanation

Helps in designing the structure (schema) of the

Database Design
database before actual implementation.

Defines Data Clearly represents how different data items relate to

Relationships each other (e.g., Student ↔ Course).

Improves Acts as a blueprint for developers, DBAs, and

Communication stakeholders to understand the system clearly.

Supports Data Helps define rules like primary keys, foreign keys, and
Integrity constraints to ensure data accuracy.

Efficient Data Enables organized data storage which makes retrieval

Access faster and more efficient.

Scalability and Makes it easier to update, expand, or modify the

Maintenance database without affecting existing data.
 Importance Explanation

Platform for DBMS Many DBMS features like query optimization, indexing,
Tools and reporting are built based on the data model.

Basic Building Blocks in DBMS

In a Database Management System (DBMS), the entire system is built on some core
components called building blocks. These define how data is stored, related, and
accessed.

1. Data

Raw facts stored in the database (e.g., numbers, names, dates)

Example: 101, Alice, 85, Computer Science

2. Table (Relation)

A table is the main structure used to store data in rows and columns.

Example:

ID Name Marks

101 Alice 85

102 Bob 90

3. Tuple (Row/Record)

A tuple is a single row in a table representing one record.

Example: 101, Alice, 85 is one tuple in the above table.

4. Attribute (Column/Field)

An attribute is a column name in a table, representing a data property.

Example: ID, Name, Marks are attributes.

5. Domain

A domain is the set of valid values an attribute can have.

Example: The domain of Marks may be from 0 to 100.

6. Schema

A schema is the overall structure/design of the database.

Example: Student(ID, Name, Marks) defines the schema for a student table.
7. Instance

An instance is the actual data stored in the database at a given moment.

Example: The contents (records) inside the Student table right now.

8. Keys

Keys are used to uniquely identify rows and maintain relationships between tables.

o Primary Key – Uniquely identifies each record

o Foreign Key – Links one table to another

What are Business Rules?

Business rules are guidelines or constraints that define how data is created,
stored, and processed in a database system according to the needs of an
organization.

They ensure that the database reflects real-world rules of the business.

Purpose of Business Rules:

o To maintain data integrity

o To reflect real-world logic in the database

o To enforce organizational policies in the database system

o To ensure accuracy and consistency of data

Examples of Business Rules:

Business Rule Explanation

A student can register for a Limits the number of entries in the

maximum of 5 courses student-course table

An employee's salary must be greater

Data validation rule
than the minimum wage

A customer cannot place an order

Enforces input constraint
without a valid email

A product code must be unique Ensures no duplicates (Primary Key)

How Business Rules are Enforced in DBMS:

Method Description

Like NOT NULL, UNIQUE, CHECK, PRIMARY KEY, FOREIGN

Constraints
KEY

Automatic actions based on specific conditions (e.g., logging

Triggers
changes)

Stored
Predefined logic to apply rules while inserting/updating data
Procedures

Application Rules enforced in the front-end application before data

Logic reaches the DBMS

Types of Business Rules:

1. Structural Rules – Define valid data structure (e.g., uniqueness of IDs)

2. Behavioral Rules – Define actions or restrictions (e.g., “Cannot delete
customer if orders exist”)

Evolution of Data Models

1. File-Based System (Before DBMS) – 1960s

 Data stored in flat files (text files)

 No relationships between data

 High redundancy, inconsistency, and difficulty in retrieval

2. Hierarchical Data Model – Late 1960s

 Data organized in a tree-like structure (parent-child)

 One-to-many relationship
 Fast access but rigid structure

Example: IBM’s Information Management System (IMS)

3. Network Data Model – 1970s

 Data in a graph structure with many-to-many relationships

 More flexible than hierarchical
 Complex to manage and navigate

Example: CODASYL DBTG model

4. Relational Data Model – 1970s (Most Popular)

 Introduced by E. F. Codd (IBM) in 1970

  Data stored in tables (relations)
 Uses SQL for querying
 Supports data independence, integrity, and easy updates

Examples: Oracle, MySQL, PostgreSQL

5. Entity-Relationship (ER) Model – 1976

 High-level conceptual model

 Uses entities, attributes, and relationships
 Useful for designing relational databases

Used in ER Diagrams during design phase

6. Object-Oriented Data Model – 1980s–1990s

 Integrates database with object-oriented programming

 Stores data as objects
 Supports inheritance, encapsulation

Used in multimedia, CAD applications

7. Semi-Structured & XML Data Models – 1990s

 For irregular and hierarchical data (e.g., web data)

 Supports tags, attributes (like in XML/JSON)
 Schema may not be fixed

Example: XML databases, JSON stores

8. NoSQL (Non-Relational) Models – 2000s–Present

 Designed for big data, distributed systems

 Supports key-value, document, column, graph models
 Highly scalable and schema-less

Examples: MongoDB, Cassandra, Neo4j

Degrees of Data Abstraction in DBMS

What is Data Abstraction?

Data Abstraction refers to the hiding of unnecessary details from the user
and showing only the essential information.It helps users interact with the system
without needing to understand complex internal details.

Purpose of Data Abstraction:

 Simplifies database usage

 Improves data security
 Separates user view from physical storage
 Makes database design more flexible and manageable
Three Levels (Degrees) of Data Abstraction

Level Description

Physical Describes how data is actually stored in the database (e.g., file
Level structure, indexing)

Logical Describes what data is stored and the relationships among the
Level data (e.g., schema)

View Describes how data is seen by end users; can have multiple
Level views for different users

Example: Student Database

Level Example Description

Physical
Data stored in files with B+ trees and hashing
Level

Logical
Table: Student(RollNo, Name, Marks) with primary key = RollNo
Level

View User sees only Name and Marks, not the internal structure or full
Level schema